
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)


from platform import python_version 
print(python_version())

3.8.8


#https://www.learnui.design/tools/data-color-picker.html#palette
#color pallette - modify here for all graphs below
colorpalette=['#000000', #PV ICE baseline
              '#0079C1','#7030A0','#F6A01A', #extreme cases (3) long life, high eff, circular
                '#41B8FF','#EF5675','#FFC425', #hypothetical modules (3) 50 yr, recycleSi, perovskite
                '#067872','#0aa39e','#09d0cd','#00ffff'] #realistic cases (4) teals, perc, shj, topcon, irena

colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B'] #colors for material plots       

import matplotlib as mpl #import matplotlib
from cycler import cycler #import cycler
mpl.rcParams['axes.prop_cycle'] = cycler(color=colorpalette) #reset the default color palette of mpl

plt.rcParams.update({'font.size': 14})
plt.rcParams['figure.figsize'] = (8, 6)

scennames_labels = ['PV_ICE','Extreme\nLong-Lived','Extreme\nHigh Eff','Extreme\nCircular',
                    'Ambitious\n50-year\nPERC','Ambitious\nRecycled Si','Ambitious\nCircular\nPerovskite',
                    'PERC','SHJ','TOPCon','IRENA\nreg. loss'] 

scennames_labels_flat = ['PV_ICE','Extreme Long-Lived','Extreme High Eff','Extreme Circular',
                    'Ambitious 50-year PERC','Ambitious Recycled Si','Ambitious Circular Perovskite',
                    'PERC','SHJ','TOPCon','IRENA reg. loss'] 
#,'Lightweight'


#creating scenarios for identical power of multiple technologies, moved to below due to reordering
scennames_ex = ['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite'] #extreme boundaries
scennames_r = ['PV_ICE','r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA'] #realistic cases
scennames_hyp = ['PV_ICE', 'h_50PERC','h_RecycledPERC', 'h_Perovskite'] #hypothetical modules


MATERIALS = ['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')


import PV_ICE
PV_ICE.__version__

'v0.2.0+446.g3a586dc.dirty'


#load in a baseline and materials for modification
sim1 = PV_ICE.Simulation(name='sim1', path=testfolder)
for scen in scennames_ex:
    sim1.createScenario(name=scen, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim1.scenario[scen].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis


sim1.scenario.keys()

dict_keys(['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite'])


celltech_modeff = pd.read_csv(os.path.join(supportMatfolder, 'output-celltech-modeffimprovements.csv'),index_col=0) #pull in module eff
celltech_aguse = pd.read_csv(os.path.join(supportMatfolder, 'output-celltech-Agusageimprovements.csv'),index_col=0) #pull in Ag use


celltech_modeff.loc[2020]

PERC      22.8
SHJ       23.5
TOPCon    23.2
Name: 2020, dtype: float64


#glass-glass package mass per area calculation
#ITRPV 2022 Figs 36 and 38, we are assuming that the front and back glass heave equal thickness of 2.5mm
density_glass = 2500*1000 # g/m^3 
glassperm2 = (2.5/1000)* 2 * density_glass
print('The mass per module area of glass is '+str(glassperm2)+' g/m^2 for all modules with a glass-glass package')

The mass per module area of glass is 12500.0 g/m^2 for all modules with a glass-glass package


timeshift = 2022-1995


#module efficiency modify for PERC
sim1.modifyScenario('ex_PERC_50', 'mod_eff', 24.5, start_year=2022) #PERC 2030 module eff


#silver modify for PERC
sim1.scenario['ex_PERC_50'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'PERC'], start_year=2022)
#old way
#sim1.scenario['PERC_50'].material['silver'].matdataIn_m.loc[timeshift:,'mat_massperm2'] = celltech_aguse.loc[2022:,'PERC'].values


#modify package to glass glass
sim1.scenario['ex_PERC_50'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022) #


#Lifetime and Degradation
#degradation rate from Theristis et al 2022
#failure is T10 at 50 years
#degradation rate:
sim1.modifyScenario('ex_PERC_50', 'mod_degradation', 0.4, start_year=2022) #annual power degradation to reach 80% at 55 yrs
#T50
sim1.modifyScenario('ex_PERC_50', 'mod_reliability_t50', 56.07, start_year=2022)
#t90
sim1.modifyScenario('ex_PERC_50', 'mod_reliability_t90', 59.15, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('ex_PERC_50', 'mod_lifetime', 50, start_year=2022) #project lifetime of 50 years


#Merchant Tail set high
sim1.modifyScenario('ex_PERC_50', 'mod_MerchantTail', 100, start_year=2022) #all installations stay for merchant tail
#Change recycling?


#idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
#df_higheff = pd.DataFrame(index=idx_temp, columns=['mod_eff_p'], dtype=float)
#df_higheff.loc[2022] = 22.0
#df_higheff.loc[2030] = 25.0
#df_higheff.loc[2050] = 30.0
#df_higheff.interpolate(inplace=True)
#module efficiency modify for PERC
sim1.modifyScenario('ex_High_eff', 'mod_eff', 30.0, start_year=2022) #changing module eff


#Lifetime and Degradation
#degradation rate:
sim1.modifyScenario('ex_High_eff', 'mod_degradation', 0.7, start_year=2022) #standard current degrdation
#T50
sim1.modifyScenario('ex_High_eff', 'mod_reliability_t50', 30.7, start_year=2022)
#t90
sim1.modifyScenario('ex_High_eff', 'mod_reliability_t90', 35, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('ex_High_eff', 'mod_lifetime', 25, start_year=2022) #project lifetime of 25 years


#modify package to glass glass
sim1.scenario['ex_High_eff'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#module efficiency modify for PERC
sim1.modifyScenario('ex_Perovskite', 'mod_eff', 27.3, start_year=2022) #changing module eff


#modify package to glass glass
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('ex_Perovskite', 'mod_degradation', 1.47, start_year=2022) #annual power degradation
#Mod Project Lifetime
sim1.modifyScenario('ex_Perovskite', 'mod_lifetime', 15, start_year=2022) #project lifetime of 15 years
#T50
sim1.modifyScenario('ex_Perovskite', 'mod_reliability_t50', 19, start_year=2022)
#t90
sim1.modifyScenario('ex_Perovskite', 'mod_reliability_t90', 22, start_year=2022)


#As Circular as possible
#100% collection rate
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_collection_eff', 100.0, start_year=2022) #100% collection
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg1_landfill', 0.0, start_year=2022) #100% collection
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb1_landfill', 0.0, start_year=2022) #100% collection

# remanufacturing
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg3_reMFG', 100.0, start_year=2022) #all modules attempt remfg
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_sp_reMFG_recycle', 100.0, start_year=2022) # recycle if can't remfg
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb3_reMFG', 100.0, start_year=2022) # remfg bad mods too
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_reMFG_yield', 98.0, start_year=2022) # REMFG YIELD 98%

#set all other paths to 0
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg0_resell', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg1_landfill', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg2_stored', 0.0, start_year=2022) #
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg4_recycled', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb1_landfill', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb2_stored', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb4_recycled', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_Repair', 0.0, start_year=2022) #
sim1.modifyScenario('ex_Perovskite', 'mod_MerchantTail', 0.0, start_year=2022) #


#Material Remanufacture
#Glass
#mfg scrap
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #99% yield
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_PG3_ReMFG_target', 100.0, start_year=2022) #send all to remfg
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send none to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_ReMFG_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild


#silicon ReMFG 
#mfg scrap
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 100.0, start_year=2022) #send to remfg
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_ReMFG_yield', 98.0, start_year=2022) #98% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 0.0, start_year=2022) #send to remfg
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#aluminium_frames recycle
#mfg scrap
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_ReMFG_yield', 0.0, start_year=2022) #0% yeild


#module efficiency modify for PERC
#sim1.modifyScenario('ex_Lightweight', 'mod_eff', celltech_modeff.loc[2022:,'PERC'], start_year=2022) #changing module eff


#2mm glass calculation
#lightweightglass = (2.0/1000)* 1 * density_glass

#50% encapsulant
#light_encap = pd.Series(sim1.scenario['PV_ICE'].material['encapsulant'].matdataIn_m.loc[timeshift:,'mat_massperm2']*0.5)

#10% silicon
#light_Si = pd.Series(sim1.scenario['PV_ICE'].material['silicon'].matdataIn_m.loc[timeshift:,'mat_massperm2']*0.1)

#modify BOM
#sim1.scenario['ex_Lightweight'].modifyMaterials('glass', 'mat_massperm2', lightweightglass, start_year=2022) #glass
#sim1.scenario['ex_Lightweight'].modifyMaterials('aluminium_frames', 'mat_massperm2', 0.0, start_year=2022) #frameless
#sim1.scenario['ex_Lightweight'].modifyMaterials('encapsulant', 'mat_massperm2', light_encap, start_year=2022) #50% encapsulant
#sim1.scenario['ex_Lightweight'].modifyMaterials('silicon', 'mat_massperm2', light_Si, start_year=2022) #10% silicon


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
#sim1.modifyScenario('ex_Lightweight', 'mod_degradation', 0.7, start_year=2022) #annual power degradation
#Mod Project Lifetime
#sim1.modifyScenario('ex_Lightweight', 'mod_lifetime', 25, start_year=2022) #project lifetime of 15 years
#T50
#sim1.modifyScenario('ex_Lightweight', 'mod_reliability_t50', 29, start_year=2022) #this will get changed with IRENA alpha/b
#t90
#sim1.modifyScenario('ex_Lightweight', 'mod_reliability_t90', 32, start_year=2022) #this will get changed with IRENA alpha/b


#len(sim1.scenario['ex_Lightweight'].material['silicon'].matdataIn_m)


#modified module
moduleinput_m_r_50PERC = os.path.join(altBaselinesfolder, 'mod_r_50PERC.csv')
sim1.createScenario(name='h_50PERC', massmodulefile=moduleinput_m_r_50PERC, energymodulefile=moduleFile_e) 

#load standard materials
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['h_50PERC'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#glass recycling
#create improvement series
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_glass_target = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_glass_target.loc[2022] = 0
df_glass_target.loc[2030] = 100
df_glass_target.loc[2050] = 100
df_glass_target.interpolate(inplace=True)

df_glass_lowrecycle = pd.DataFrame(index=idx_temp, columns=['mat_EOL_Recycled_into_HQ'], dtype=float)
df_glass_lowrecycle.loc[2022] = 0
df_glass_lowrecycle.loc[2030] = 0
df_glass_lowrecycle.loc[2050] = 50
df_glass_lowrecycle.interpolate(inplace=True)

#eol
sim1.scenario['h_50PERC'].modifyMaterials('glass', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_50PERC'].modifyMaterials('glass', 'mat_PG4_Recycling_target', 
                                          df_glass_target.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) #send to recycle

sim1.scenario['h_50PERC'].modifyMaterials('glass', 'mat_EOL_Recycled_into_HQ', 
                                          df_glass_lowrecycle.loc[2022:,'mat_EOL_Recycled_into_HQ'], start_year=2022) #all HQ
sim1.scenario['h_50PERC'].modifyMaterials('glass', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#silicon recycled
#create improvement series
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_si_target = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_si_target.loc[2022] = 0
df_si_target.loc[2030] = 0
df_si_target.loc[2050] = 50
df_si_target.interpolate(inplace=True)

#eol
sim1.scenario['h_50PERC'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_50PERC'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 
                                          df_si_target.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) #send to recycle

sim1.scenario['h_50PERC'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_50PERC'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#aluminium_frames recycle
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_al_target = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_al_target.loc[2022] = 0
df_al_target.loc[2030] = 100
df_al_target.loc[2050] = 100
df_al_target.interpolate(inplace=True)

df_al_lowrecycle = pd.DataFrame(index=idx_temp, columns=['mat_EOL_Recycled_into_HQ'], dtype=float)
df_al_lowrecycle.loc[2022] = 0
df_al_lowrecycle.loc[2030] = 100
df_al_lowrecycle.loc[2050] = 100
df_al_lowrecycle.interpolate(inplace=True)

df_al_lowCL = pd.DataFrame(index=idx_temp, columns=['mat_EOL_RecycledHQ_Reused4MFG'], dtype=float)
df_al_lowCL.loc[2022] = 0
df_al_lowCL.loc[2030] = 20
df_al_lowCL.loc[2050] = 50
df_al_lowCL.interpolate(inplace=True)

#eol
sim1.scenario['h_50PERC'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_50PERC'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 
                                          df_al_target.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) #send to recycle
sim1.scenario['h_50PERC'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_50PERC'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 
                                          df_al_lowrecycle.loc[2022:,'mat_EOL_Recycled_into_HQ'], start_year=2022) #all HQ
sim1.scenario['h_50PERC'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 
                                          df_al_lowCL.loc[2022:,'mat_EOL_RecycledHQ_Reused4MFG'], start_year=2022) #closed-loop


#modified module
moduleinput_m_r_RecyclePERC = os.path.join(altBaselinesfolder, 'mod_r_PERCrecycleSi.csv')
sim1.createScenario(name='h_RecycledPERC', massmodulefile=moduleinput_m_r_RecyclePERC, energymodulefile=moduleFile_e) 

for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['h_RecycledPERC'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#glass recycling
#eol
sim1.scenario['h_RecycledPERC'].modifyMaterials('glass', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('glass', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
#sim1.scenario['h_RecycledPERC'].modifyMaterials('glass', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_RecycledPERC'].modifyMaterials('glass', 'mat_EOL_Recycled_into_HQ', 0.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('glass', 'mat_EOL_RecycledHQ_Reused4MFG', 0.0, start_year=2022) #closed-loop

#silicon recycled
#mfg scrap
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop

#aluminium_frames recycle
#mfg scrap
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop

#silver recycling improvement
#create improvement series
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_silver_recycleimprove = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_silver_recycleimprove.loc[2022] = 0
df_silver_recycleimprove.loc[2030] = 40
df_silver_recycleimprove.loc[2050] = 100
df_silver_recycleimprove.interpolate(inplace=True)

#eol
sim1.scenario['h_RecycledPERC'].modifyMaterials('silver', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('silver', 'mat_PG4_Recycling_target',
                                        df_silver_recycleimprove.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) 
sim1.scenario['h_RecycledPERC'].modifyMaterials('silver', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_RecycledPERC'].modifyMaterials('silver', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('silver', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#the cool feature of this module was that the wafer was directly put into the Cz process
#therefore, we need to modify the recycling energy to reflect this
#this energy profile is output by the (baseline development) Energy Silicon Manufacturing journal.
altHQRecycle_e = pd.read_csv(os.path.join(supportMatfolder, 'output_energy_silicon_eol_recycleHQ_ALT.csv'), index_col=0)
altHQRecycle_e


#modify silicon recycling energy
sim1.scenario['h_RecycledPERC'].modifyMaterialEnergy('silicon', 'e_mat_Recycled_HQ', altHQRecycle_e.loc[2022:,'E_MFG_kWhpkg'], start_year=2022)
sim1.scenario['h_RecycledPERC'].modifyMaterialEnergy('silicon', 'e_mat_Recycled_HQ_fuelfraction', altHQRecycle_e.loc[2022:,'E_mfgFuelFraction'], start_year=2022)


sim1.scenario['h_RecycledPERC'].material['silicon'].matdataIn_e.iloc[25:30,]


#modified module
moduleinput_m_r_perovskite = os.path.join(altBaselinesfolder, 'mod_r_perovskite.csv')
sim1.createScenario(name='h_Perovskite', massmodulefile=moduleinput_m_r_perovskite, energymodulefile=moduleFile_e) 

#modified materials
#glass
matinput_glass_m_remfg = os.path.join(altBaselinesfolder,'material_mass_glass_r_perovskite.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['h_Perovskite'].addMaterial('glass', massmatfile=matinput_glass_m_remfg, energymatfile=matbaseline_e_glass)

#silicon
matinput_silicon_m_remfg = os.path.join(altBaselinesfolder,'material_mass_silicon_r_perovskite.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['h_Perovskite'].addMaterial('silicon', massmatfile=matinput_silicon_m_remfg, energymatfile=matbaseline_e_si)

#load standard materials
subMats =['silver', 'aluminium_frames','copper','encapsulant', 'backsheet']
for mat in range (0, len(subMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+subMats[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+subMats[mat]+'.csv')
    sim1.scenario['h_Perovskite'].addMaterial(subMats[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#aluminium_frames recycle
#eol
sim1.scenario['h_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['h_Perovskite'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG',100.0, start_year=2022) #closed-loop


altBaselinesfolder

'C:\\Users\\hmirletz\\Documents\\GitHub\\PV_ICE\\PV_ICE\\baselines\\Energy_CellModuleTechCompare'


#modified module
moduleinput_m_r_PERC = os.path.join(altBaselinesfolder, 'mod_r_PERC.csv')
sim1.createScenario(name='r_PERC', massmodulefile=moduleinput_m_r_PERC, energymodulefile=moduleFile_e)


#module efficiency modify for PERC
sim1.modifyScenario('r_PERC', 'mod_eff', celltech_modeff.loc[2022:,'PERC'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_PERC'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_PERC'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_PERC'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_PERC'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_PERC'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#modify silver usage for PERC
sim1.scenario['r_PERC'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'PERC'], start_year=2022)


#modify package to glass glass
sim1.scenario['r_PERC'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#modified module
moduleinput_m_r_SHJ = os.path.join(altBaselinesfolder, 'mod_r_SHJ.csv')
sim1.createScenario(name='r_SHJ', massmodulefile=moduleinput_m_r_SHJ, energymodulefile=moduleFile_e)


#module efficiency modify for SHJ
sim1.modifyScenario('r_SHJ', 'mod_eff', celltech_modeff.loc[2022:,'SHJ'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#load modified materials
#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_SHJ'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_SHJ'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_SHJ'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_SHJ'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_SHJ'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#modify silver usage for SHJ
sim1.scenario['r_SHJ'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'SHJ'], start_year=2022)


#modify package to glass glass
sim1.scenario['r_SHJ'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#modified module
moduleinput_m_r_TOPCon = os.path.join(altBaselinesfolder, 'mod_r_TOPCon.csv')
sim1.createScenario(name='r_TOPCon', massmodulefile=moduleinput_m_r_TOPCon, energymodulefile=moduleFile_e)


#module efficiency modify
sim1.modifyScenario('r_TOPCon', 'mod_eff', celltech_modeff.loc[2022:,'TOPCon'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_TOPCon'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_TOPCon'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_TOPCon'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_TOPCon'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_TOPCon'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#modify silver usage for PERC
sim1.scenario['r_TOPCon'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'TOPCon'], start_year=2022)


#modify package to glass glass
sim1.scenario['r_TOPCon'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#modified module
moduleinput_m_r_IRENA = os.path.join(altBaselinesfolder, 'mod_r_IRENAregloss.csv')
sim1.createScenario(name='r_IRENA', massmodulefile=moduleinput_m_r_IRENA, energymodulefile=moduleFile_e)


#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_IRENA'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_IRENA'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_IRENA'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_IRENA'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_IRENA'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim1.trim_Years(startYear=2000, endYear=2100)

Trimming and extending  PV_ICE
PV_ICE glass : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silicon : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE silver : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE copper : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  ex_PERC_50
ex_PERC_50 glass : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 silicon : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 silver : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 copper : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  ex_High_eff
ex_High_eff glass : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff silicon : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff silver : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff copper : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  ex_Perovskite
ex_Perovskite glass : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite silicon : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite silver : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite copper : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  h_50PERC
h_50PERC glass : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC silicon : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC silver : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC copper : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  h_RecycledPERC
h_RecycledPERC glass : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC silicon : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC silver : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC copper : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  h_Perovskite
h_Perovskite glass : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite silicon : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite silver : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite copper : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_PERC
r_PERC glass : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC silicon : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC silver : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC copper : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_SHJ
r_SHJ glass : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ silicon : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ silver : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ copper : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_TOPCon
r_TOPCon glass : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon silicon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon silver : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon copper : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon backsheet : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_IRENA
r_IRENA glass : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA silicon : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA silver : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA aluminium_frames : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA copper : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA encapsulant : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA backsheet : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA backsheet : Data trimmed for Mass, years now encompass  2000  to  2100


#check
#sim1.scenario['h_Perovskite'].dataIn_m


global_projection = pd.read_csv(os.path.join(supportMatfolder,'output-globalInstallsProjection.csv'), index_col=0)

fig, ax1 = plt.subplots()

ax1.stackplot(global_projection.index, global_projection['World_cum']/1e6, color='#F7A11A')
ax1.set_ylabel('Cumulative Solar Capacity [TW]', color='#F7A11A')
ax1.set_ylim(0,90)
ax1.set_xlim(2000,2100)
ax2 = ax1.twinx()
ax2.plot(global_projection['World_annual_[MWdc]']/1e6)
ax2.set_ylabel('Annual Installations [TW]')
ax2.set_ylim(0,5)
plt.show()

fig.savefig('energyresults-deployment.png', dpi=300, bbox_inches='tight')


#deployment projection for all scenarios
sim1.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', 
                    value= global_projection['World_annual_[MWdc]'], start_year=2000)
#for scen in scennames:
#    sim1.scenario[scen].dataIn_m.loc[0:len(global_projection.index-1),'new_Installed_Capacity_[MW]'] = global_projection['World_annual_[MWdc]'].values


sim1.scenario.keys()

dict_keys(['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite', 'h_50PERC', 'h_RecycledPERC', 'h_Perovskite', 'r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA'])


scennames_ex = ['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite'] #extreme boundaries
scennames_r = ['PV_ICE','r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA'] #realistic cases
scennames_hyp = ['PV_ICE', 'h_50PERC','h_RecycledPERC', 'h_Perovskite'] #hypothetical modules

scennames_all = ['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite',
                 'h_50PERC','h_RecycledPERC', 'h_Perovskite',
                'r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA']


for scen in sim1.scenario.keys():
    print(sim1.scenario[scen].material.keys())

dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])


IRENAregloss = {'alpha':5.3759,
                   'beta':30} #IRENA values


bifiFactors = {'PV_ICE':0.0,
               'ex_PERC_50':0.7, # ITRPV 2022, Fig. 58
               'ex_High_eff': 0.9,
               'ex_Perovskite': 0.0,
               'h_50PERC':0.7,
               'h_RecycledPERC': 0.6,
               'h_Perovskite': 0.0,
               'r_PERC':0.7,
               'r_SHJ':0.9, # ITRPV 2022, Fig. 58
               'r_TOPCon':0.8, # ITRPV 2022, Fig. 58              
                'r_IRENA':0.0 } 

#MAY NEED TO CHANGE TO BE DYNAMIC
#               'ex_Lightweight':0.0,


#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
idx_temp = pd.RangeIndex(start=2000,stop=2101,step=1) #create the index
df_temp = pd.DataFrame(index=idx_temp, columns=['bifi'], dtype=float)
bifiPathDict={}

for f in bifiFactors.keys(): #loop over module types
    bifi = df_temp.copy() #copy of df
    bifi['bifi'] = bifiFactors[f] #assign column
    bifipath = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi.to_csv(path_or_buf=bifipath, index_label='year') #create file
    bifiPathDict[str(f)] = bifipath
    #append bifi path to dict? or list?


for scen in sim1.scenario.keys(): #loop over scenarios
    if scen in ['r_IRENA']: #,'ex_Lightweight'
        sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen], weibullInputParams=IRENAregloss)
    #elif scen in ['ex_PERC_50','h_50PERC']:
    #    sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen], nameplatedeglimit=0.0) 
    else:
        sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_PERC_50
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_High_eff
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_Perovskite
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
ReMFG surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  2286015779.360064  tonnes.
Recycled surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  11399047.994346652  tonnes.
==> Working on Material :  silicon
ReMFG surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  60979171.60898491  tonnes.
Recycled surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  267559.9561905653  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  ex_Perovskite  =  215480556.58063576  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  h_50PERC
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  h_50PERC  =  6965632.679115519  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  h_50PERC  =  2591670.1919490653  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  h_50PERC  =  24793365.506877523  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  h_RecycledPERC
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  h_RecycledPERC  =  85989051.91329204  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  h_RecycledPERC  =  466988.9573589953  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  h_RecycledPERC  =  295432881.60088825  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  h_Perovskite
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
ReMFG surplus End of Sim for Mat  glass  Scenario  h_Perovskite  =  2084001632.3159513  tonnes.
Recycled surplus End of Sim for Mat  glass  Scenario  h_Perovskite  =  5602589.40619942  tonnes.
==> Working on Material :  silicon
ReMFG surplus End of Sim for Mat  silicon  Scenario  h_Perovskite  =  46772470.20863377  tonnes.
Recycled surplus End of Sim for Mat  silicon  Scenario  h_Perovskite  =  1865534.95208879  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  h_Perovskite  =  241617026.8598689  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  r_PERC
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_PERC  =  1254222924.476181  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_PERC  =  35652809.92358141  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_PERC  =  209792.68009645375  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_PERC  =  188309737.96324486  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  r_SHJ
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_SHJ  =  1192345750.163131  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_SHJ  =  33894036.79518448  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_SHJ  =  369744.7050073073  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_SHJ  =  179020152.35583487  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  r_TOPCon
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_TOPCon  =  1217860187.5683703  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_TOPCon  =  34619398.884777226  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_TOPCon  =  355884.5768220496  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_TOPCon  =  182851261.423886  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  r_IRENA
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_IRENA  =  1234805768.2411082  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_IRENA  =  43197078.41449269  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_IRENA  =  255968.74065249972  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_IRENA  =  229395718.3704415  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


ii_yearly, ii_cumu = sim1.aggregateResults() #have to do this to get auto plots


sim1.saveSimulation(customname='_EnergyAnalysis_identicalinstalls')

:) Saved Input and Output Dataframes


effective_capacity = ii_yearly.filter(like='ActiveCapacity')
#plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')
#plt.plot(effective_capacity/1e6, label=sim1.scenario.keys())
#plt.legend()
#plt.ylabel('Effective Capacity [TW]')
#plt.title('Effective Capacity: No Replacements')
#plt.ylim(0,)


effective_capacity_tw = ii_yearly.filter(like='ActiveCapacity')/1e6

plt.rcParams['figure.figsize'] = (10, 6)

plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, 
         label='Capacity Target', color='black', ls='dashdot')

plt.plot(effective_capacity_tw.iloc[:,0:4], label=scennames_labels_flat[0:4]) #color=colorpalette[1:4], 
#create glowlines for idealized scens
n_lines = 10
diff_linewidth = 1.05
alpha_value = 0.05
for n in range(1, n_lines+1):    
    plt.plot(effective_capacity_tw.iloc[:,1],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[1])

for n in range(1, n_lines+1):    
    plt.plot(effective_capacity_tw.iloc[:,2],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[2])

for n in range(1, n_lines+1):    
    plt.plot(effective_capacity_tw.iloc[:,3],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[3])

plt.plot(effective_capacity_tw.iloc[:,4:7], label=scennames_labels_flat[4:7],  ls='--') #color=colorpalette[4:7],
plt.plot(effective_capacity_tw.iloc[:,7:], label=scennames_labels_flat[7:])#, color=colorpalette[7:])

#plt.plot(effective_capacity/1e6, label=sim1.scenario.keys())
plt.legend(prop={'size': 11}) #bbox_to_anchor=(1,1.02), 
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)
plt.xlim(2000,2100)

plt.savefig('energyresults-effectivecapacity.png', dpi=300, bbox_inches='tight')


plt.rcParams['figure.figsize'] = (8, 6)


#currently takes ~40 mins to run with 7 materials and 12 scenarios
UnderInstall_df = pd.DataFrame()


for row in range (0,len(sim1.scenario['PV_ICE'].dataIn_m)): #loop over length of years
    print(row)
    for scen in sim1.scenario.keys(): #loop over scenarios
        print(scen)
        Under_Installment = global_projection.iloc[row,0] - ((sim1.scenario[scen].dataOut_m['Effective_Capacity_[W]'][row])/1e6)  # MWATTS
        sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
        UnderInstall_df.loc[row,scen] = Under_Installment #save the underinstallment as df
        #calculate flows for that scenario with it's bifi factor and modified weibull
        if scen in ['r_IRENA']: #,'ex_Lightweight'
            sim1.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict[scen], weibullInputParams=IRENAregloss)
        #elif scen in ['ex_PERC_50','h_50PERC']:
        #    sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen], nameplatedeglimit=0.0) 
        else:
            sim1.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

0
PV_ICE
>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10680\1411915970.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  PV_ICE
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
ex_PERC_50
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_PERC_50
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10680\1411915970.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
ex_High_eff
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_High_eff
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10680\1411915970.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  ex_High_eff
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
ex_Perovskite
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_Perovskite
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10680\1411915970.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
ReMFG surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  2286015779.360064  tonnes.
Recycled surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  11399047.994346652  tonnes.
==> Working on Material :  silicon
ReMFG surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  60979171.60898491  tonnes.
Recycled surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  267559.9561905653  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  ex_Perovskite  =  215480556.58063576  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  ex_Perovskite
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
h_50PERC
>>>> Calculating Material Flows <<<<

Working on Scenario:  h_50PERC
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_10680\1411915970.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed


#test with one scenario
#scen='r_Perovskite'
#df_compensate = pd.DataFrame(columns=['underinstalls[W]'],dtype=float)

#for row in range (0,len(sim1.scenario['PV_ICE'].dataIn_m)): #loop over length of years
#    print(row)
#    Under_Installment = global_projection.iloc[row,0] - ((sim1.scenario[scen].dataOut_m['Installed_Capacity_[W]'][row])/1e6)  # MWATTS
#    if row==0:
#        df_compensate.loc[row,'underinstalls[W]'] = Under_Installment
#    else:
#        if Under_Installment < 0.7*df_compensate.iloc[row-1,0]:
#            Under_Installment = df_compensate.iloc[row-1,0]
#    df_compensate.loc[row,'underinstalls[W]'] = Under_Installment
#    sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
    #calculate flows for that scenario with it's bifi factor and modified weibull
#    sim1.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])


#aggregate results - mass
cc_yearly, cc_cumu = sim1.aggregateResults() #have to do this to get auto plots
cc_yearly.to_csv(os.path.join(testfolder, 'cc_10scen_yearly.csv'))
cc_cumu.to_csv(os.path.join(testfolder, 'cc_10scen_cumu.csv'))


#aggregate results - energy
allenergy, energyGen, energy_demands = sim1.aggregateEnergyResults()
allenergy.to_csv(os.path.join(testfolder, 'cc_10scen_allenergy.csv'))
energyGen.to_csv(os.path.join(testfolder, 'cc_10scen_energyGen.csv'))
energy_demands.to_csv(os.path.join(testfolder, 'cc_10scen_energy_demands.csv'))

UnderInstall_df.to_csv(os.path.join(testfolder, 'cc_10scen_underInstalls.csv'))

C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2177: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)


sim1.saveSimulation(customname='_EnergyAnalysis_withreplacements')

:) Saved Input and Output Dataframes


#read in saved results files for speed
cc_yearly = pd.read_csv(os.path.join(testfolder, 'cc_10scen_yearly.csv'), index_col='year')
cc_cumu = pd.read_csv(os.path.join(testfolder, 'cc_10scen_cumu.csv'), index_col='year')
allenergy = pd.read_csv(os.path.join(testfolder, 'cc_10scen_allenergy.csv'), index_col='year')
energyGen = pd.read_csv(os.path.join(testfolder, 'cc_10scen_energyGen.csv'), index_col='year')
energy_demands = pd.read_csv(os.path.join(testfolder, 'cc_10scen_energy_demands.csv'), index_col='year')


plt.plot(sim1.scenario['r_PERC'].dataOut_m['Effective_Capacity_[W]'])

[<matplotlib.lines.Line2D at 0x1e148ee4880>]


#sim1.scenario['PV_ICE'].dataOut_m['irradiance_stc'].head(2)
sim1.scenario.keys()

dict_keys(['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite', 'h_50PERC', 'h_RecycledPERC', 'h_Perovskite', 'r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA'])


UnderInstall_df_temp = cc_yearly.filter(like='newInstalledCapacity_').subtract(global_projection[['World_annual_[MWdc]']].values)


#Attempt to make the deployment less dumb

UnderInstall_df_cumu = UnderInstall_df_temp.cumsum()

flatinstall = UnderInstall_df_cumu.iloc[-1,:]/(2100-2022)
flatinstall

newInstalledCapacity_sim1_PV_ICE_[MW]            1.342246e+06
newInstalledCapacity_sim1_ex_PERC_50_[MW]        1.026676e+06
newInstalledCapacity_sim1_ex_High_eff_[MW]       2.244034e+06
newInstalledCapacity_sim1_ex_Perovskite_[MW]     4.027728e+06
newInstalledCapacity_sim1_h_50PERC_[MW]          1.035524e+06
newInstalledCapacity_sim1_h_RecycledPERC_[MW]    1.512689e+06
newInstalledCapacity_sim1_h_Perovskite_[MW]      2.454031e+06
newInstalledCapacity_sim1_r_PERC_[MW]            1.306151e+06
newInstalledCapacity_sim1_r_SHJ_[MW]             1.306151e+06
newInstalledCapacity_sim1_r_TOPCon_[MW]          1.306151e+06
newInstalledCapacity_sim1_r_IRENA_[MW]           2.397069e+06
Name: 2100, dtype: float64


sim1.scenario['h_RecycledPERC'].dataOut_m['WeibullParams'].tail(4)

97     {'alpha': 12.283636210794834, 'beta': 42.04601...
98     {'alpha': 12.283636210794834, 'beta': 42.04601...
99     {'alpha': 12.283636210794834, 'beta': 42.04601...
100    {'alpha': 12.283636210794834, 'beta': 42.04601...
Name: WeibullParams, dtype: object


effective_capacity = cc_yearly.filter(like='ActiveCapacity')
plt.plot(effective_capacity/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: With Replacements')
plt.ylim(0,)

(0.0, 90.26425956655)


annual_EoL = cc_yearly.filter(like='DecommisionedCapacity')
plt.plot(annual_EoL/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Annual EoL [TW]')
plt.title('Annual Decommissions [TW]')
plt.ylim(0,)

(0.0, 329.87090864379934)


annual_installs = cc_yearly.filter(like='newInstalled')
plt.plot(annual_installs/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Annual installed [TW]')
plt.title('Annual Installs with Replacements')
plt.ylim(0,)

(0.0, 8.61667530426398)


annual_installs_TW = cc_yearly.filter(like='newInstalled')/1e6

plt.plot(global_projection['World_annual_[MWdc]']/1e6, label='Min. Deployment', ls='dashdot')
plt.plot(annual_installs_TW.iloc[:,1:4], label = scennames_labels_flat[1:4])
#create glowlines for idealized scens
n_lines = 10
diff_linewidth = 1.05
alpha_value = 0.05
for n in range(1, n_lines+1):    
    plt.plot(annual_installs_TW.iloc[:,1],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[1])

for n in range(1, n_lines+1):    
    plt.plot(annual_installs_TW.iloc[:,2],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[2])

for n in range(1, n_lines+1):    
    plt.plot(annual_installs_TW.iloc[:,3],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[3])

plt.legend()
plt.ylabel('Annual installed [TW]')
plt.title('Annual Installs with Replacements')
plt.ylim(0,)
plt.xlim(2000,2100)

plt.savefig('energyresults-annualdeployment.png', dpi=300, bbox_inches='tight')


annual_installs_TW = cc_yearly.filter(like='newInstalled')/1e6

fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5), sharey=True, sharex=True, 
                                      gridspec_kw={'wspace':0})
ax1.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax1.set_prop_cycle(color=colorpalette[1:4])
ax1.plot(annual_installs_TW.iloc[:,1:4], label=scennames_labels_flat[1:4])# , color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)
ax1.set_ylabel('Annual installed [TW]', fontsize=20)
ax1.set_xlim(2000,2100)
ax1.set_ylim(0,8.5)
ax1.legend(bbox_to_anchor=(0.9,-0.05))

ax2.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax2.set_prop_cycle(color=colorpalette[4:7])
ax2.plot(annual_installs_TW.iloc[:,4:7], label=scennames_labels_flat[4:7], ls='--') #, color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)
ax2.legend(bbox_to_anchor=(1,-0.05))

ax3.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax3.set_prop_cycle(color=colorpalette[7:11])
ax3.plot(annual_installs_TW.iloc[:,7:11], label=scennames_labels_flat[7:11])# , color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)
ax3.legend(bbox_to_anchor=(0.9,-0.05))

fig.suptitle('Annual Installs with Replacements', fontsize=24)
plt.show()
fig.savefig('energyresults-annualInstalls.png', dpi=300, bbox_inches='tight')


posttransinstalls = annual_installs.loc[2051:,].cumsum()
flatinstallslate = posttransinstalls.loc[2100]/(2100-2050)
flatinstallslate

newInstalledCapacity_sim1_PV_ICE_[MW]            2.221055e+06
newInstalledCapacity_sim1_ex_PERC_50_[MW]        1.742914e+06
newInstalledCapacity_sim1_ex_High_eff_[MW]       3.572753e+06
newInstalledCapacity_sim1_ex_Perovskite_[MW]     5.963659e+06
newInstalledCapacity_sim1_h_50PERC_[MW]          1.756639e+06
newInstalledCapacity_sim1_h_RecycledPERC_[MW]    2.436506e+06
newInstalledCapacity_sim1_h_Perovskite_[MW]      3.711676e+06
newInstalledCapacity_sim1_r_PERC_[MW]            2.159963e+06
newInstalledCapacity_sim1_r_SHJ_[MW]             2.159963e+06
newInstalledCapacity_sim1_r_TOPCon_[MW]          2.159963e+06
newInstalledCapacity_sim1_r_IRENA_[MW]           3.757768e+06
Name: 2100, dtype: float64


annual_installs_modify = annual_installs.copy()
for col in annual_installs_modify:
    annual_installs_modify.loc[2051:,col] = flatinstallslate[col]

#annual_installs_modify
#plt.plot(annual_installs_modify)


#annual_installs_TW = cc_yearly.filter(like='newInstalled')
annual_installs_modify_TW = annual_installs_modify/1e6

fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5), sharey=True, sharex=True, 
                                      gridspec_kw={'wspace':0})
ax1.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax1.set_prop_cycle(color=colorpalette[1:4])
ax1.plot(annual_installs_TW.iloc[:,1:4], label=scennames_labels_flat[1:4])# , color=colorpalette[1:4])
ax1.plot(annual_installs_modify_TW.iloc[:,1:4], label=scennames_labels_flat[1:4])# , color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)
ax1.set_ylabel('Annual installed [TW]', fontsize=20)
ax1.set_xlim(2000,2100)
ax1.set_ylim(0,8.5)
ax1.legend(bbox_to_anchor=(0.9,-0.05))

ax2.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax2.set_prop_cycle(color=colorpalette[4:7])
ax2.plot(annual_installs_TW.iloc[:,4:7], label=scennames_labels_flat[4:7], ls='--')
ax2.plot(annual_installs_modify_TW.iloc[:,4:7], label=scennames_labels_flat[4:7], ls='--') #, color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)
ax2.legend(bbox_to_anchor=(1,-0.05))

ax3.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax3.set_prop_cycle(color=colorpalette[7:11])
ax3.plot(annual_installs_TW.iloc[:,7:11], label=scennames_labels_flat[7:11])
ax3.plot(annual_installs_modify_TW.iloc[:,7:11], label=scennames_labels_flat[7:11])# , color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)
ax3.legend(bbox_to_anchor=(0.9,-0.05))

fig.suptitle('Annual Installs with Replacements Averaged Post 2050', fontsize=24)
plt.show()
fig.savefig('energyresults-annualInstalls-AVG.png', dpi=300, bbox_inches='tight')


#decade binning of post 2050 installs by decade
posttransinstalls_annual = annual_installs.loc[2051:,]
posttransinstalls_decades = posttransinstalls_annual.groupby(np.arange(len(posttransinstalls_annual))//10).sum()
posttransinstalls_decades.index = ['2051-2060','2061-2070','2071-2080','2081-2090','2091-2100']
posttransinstalls_decades
#this is the sum each decade, still need to divide by 10 for annual


annual_installs_decademod = annual_installs.copy()
for col in annual_installs_decademod:
    annual_installs_decademod.loc[2051:2060,col] = posttransinstalls_decades.loc['2051-2060',col]/10
    annual_installs_decademod.loc[2061:2070,col] = posttransinstalls_decades.loc['2061-2070',col]/10
    annual_installs_decademod.loc[2071:2080,col] = posttransinstalls_decades.loc['2071-2080',col]/10
    annual_installs_decademod.loc[2081:2090,col] = posttransinstalls_decades.loc['2081-2090',col]/10
    annual_installs_decademod.loc[2091:2100,col] = posttransinstalls_decades.loc['2091-2100',col]/10
    
#plt.plot(annual_installs_decademod)


#annual_installs_TW = cc_yearly.filter(like='newInstalled')
annual_installs_decademod_TW = annual_installs_decademod/1e6

fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5), sharey=True, sharex=True, 
                                      gridspec_kw={'wspace':0})
ax1.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax1.set_prop_cycle(color=colorpalette[1:4])
#ax1.plot(annual_installs_TW.iloc[:,1:4], label=scennames_labels_flat[1:4])# , color=colorpalette[1:4])
ax1.plot(annual_installs_decademod_TW.iloc[:,1:4], label=scennames_labels_flat[1:4])# , color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)
ax1.set_ylabel('Annual installed [TW]', fontsize=20)
ax1.set_xlim(2000,2100)
ax1.set_ylim(0,8.5)
ax1.legend(bbox_to_anchor=(0.9,-0.05))

ax2.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax2.set_prop_cycle(color=colorpalette[4:7])
#ax2.plot(annual_installs_TW.iloc[:,4:7], label=scennames_labels_flat[4:7], ls='--')
ax2.plot(annual_installs_decademod_TW.iloc[:,4:7], label=scennames_labels_flat[4:7], ls='--') #, color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)
ax2.legend(bbox_to_anchor=(1,-0.05))

ax3.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax3.set_prop_cycle(color=colorpalette[7:11])
#ax3.plot(annual_installs_TW.iloc[:,7:11], label=scennames_labels_flat[7:11])
ax3.plot(annual_installs_decademod_TW.iloc[:,7:11], label=scennames_labels_flat[7:11])# , color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)
ax3.legend(bbox_to_anchor=(0.9,-0.05))

fig.suptitle('Annual Installs with Replacements Decade Average Post 2050', fontsize=24)
plt.show()
fig.savefig('energyresults-annualInstalls-decade.png', dpi=300, bbox_inches='tight')


cumu_installs_annually = cc_cumu.filter(like='newInstalled')
plt.plot(cumu_installs_annually/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Cumulative installed [TW]')
plt.title('Replacements Adjusted Deployment Curve \n Cumulative Installs with Replacements')
plt.ylim(0,)

(0.0, 420.13516821034926)


cumu_installs = cc_cumu.filter(like='newInstalled')
plt.bar(scennames_labels, cumu_installs.loc[2100]/1e6, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.xticks(rotation=90)
plt.ylabel('Cumulative installed [TW]')
plt.title('Cumulative Installs with Replacements')
plt.ylim(0,410)

(0.0, 410.0)


replacements = cumu_installs.iloc[-1,]/1e6
replacements.index = scennames_labels


cumu_installs = cc_cumu.filter(like='newInstalled')

fig, (ax0,ax1,ax2,ax3) = plt.subplots(1,4,figsize=(15,6), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [0.5,2,2,2.5]})

ax0.bar(scennames_labels[0:1], cumu_installs.iloc[-1,0:1]/1e6, width=0.1, 
        tick_label=scennames_labels[0:1], color=colorpalette[0:1])
ax0.set_ylim(0,410)
ax0.set_ylabel('Cumulative installed [TW]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)

ax1.bar(scennames_labels[1:4], cumu_installs.iloc[-1,1:4]/1e6, width=0.8, 
        tick_label=scennames_labels[1:4], color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)

ax2.bar(scennames_labels[4:7], cumu_installs.iloc[-1,4:7]/1e6, width=0.8,
        tick_label=scennames_labels[4:7], color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)

ax3.bar(scennames_labels[7:11], cumu_installs.iloc[-1,7:11]/1e6, width=0.8,
        tick_label=scennames_labels[7:11], color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)

fig.suptitle('Cumulative Installs with Replacements', fontsize=24)
plt.show()

fig.savefig('energyresults-cumulativedeploy.png', dpi=300, bbox_inches='tight')


cumulative_nameplate_installs = global_projection.loc[2100,'World_cum'] #MW
print('The nameplate installations for energy transition and through 2100 are '+str(cumulative_nameplate_installs/1e6)+' TW.')

The nameplate installations for energy transition and through 2100 are 85.966 TW.


global_projection['World_annual_[MWdc]'].sum()

85966000.0


Additional_installs = cumu_installs.loc[2100]-global_projection.loc[2100,'World_cum']
plt.bar(scennames_labels, Additional_installs/1e6, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.ylabel('Cumulative Replacements [TW]')
plt.title('Replacements Required by Technology')
plt.xticks(rotation=90)
plt.ylim(0,410)

(0.0, 410.0)


cumu_virgin_module = cc_cumu.filter(like='VirginStock_Module')
plt.bar(scennames_labels, cumu_virgin_module.loc[2100]/1e9, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.title('Cumulative Virgin Material Demands')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, 'Extreme\nLong-Lived'),
  Text(2.0, 0, 'Extreme\nHigh Eff'),
  Text(3.0, 0, 'Extreme\nCircular'),
  Text(4.0, 0, 'Ambitious\n50-year\nPERC'),
  Text(5.0, 0, 'Ambitious\nRecycled Si'),
  Text(6.0, 0, 'Ambitious\nCircular\nPerovskite'),
  Text(7.0, 0, 'PERC'),
  Text(8.0, 0, 'SHJ'),
  Text(9.0, 0, 'TOPCon'),
  Text(10.0, 0, 'IRENA\nreg. loss')])


cumu_virgin_module_bt = cc_cumu.filter(like='VirginStock_Module')/1e9

fig, (ax0,ax1,ax2,ax3) = plt.subplots(1,4,figsize=(18,7), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [0.5,2,2,2.5]})

ax0.bar(scennames_labels[0:1], cumu_virgin_module_bt.iloc[-1,0:1], width=0.1, 
        tick_label=scennames_labels[0:1], color=colorpalette[0:1])
ax0.set_ylim(0,12)
ax0.set_ylabel('Virgin Material Requirements\n[billion tonnes]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)

ax1.bar(scennames_labels[1:4], cumu_virgin_module_bt.iloc[-1,1:4], width=0.8, 
        tick_label=scennames_labels[1:4], color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)

ax2.bar(scennames_labels[4:7], cumu_virgin_module_bt.iloc[-1,4:7], width=0.8,
        tick_label=scennames_labels[4:7], color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)

ax3.bar(scennames_labels[7:11], cumu_virgin_module_bt.iloc[-1,7:11], width=0.8,
        tick_label=scennames_labels[7:11], color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)

fig.suptitle('Cumulative Virgin Material Demands', fontsize=24)
plt.show()

fig.savefig('energyresults-cumuMatDemand.png', dpi=300, bbox_inches='tight')


virgin = cumu_virgin_module_bt.iloc[-1,:]
virgin.index=scennames_labels


recycledperc_virginstock = cc_cumu.filter(like='VirginStock').filter(like='Recycled')
recycledperc_virginstock.drop('VirginStock_Module_sim1_h_RecycledPERC_[Tonnes]',axis=1, inplace=True)
plt.bar(recycledperc_virginstock.columns, recycledperc_virginstock.loc[2100]/1e6, tick_label = MATERIALS, color=colormats)
plt.ylabel('Million Metric tonnes')

Text(0, 0.5, 'Million Metric tonnes')


cumu_virgin_module = cc_cumu.filter(like='VirginStock_Module')
plt.plot(cumu_virgin_module/1e9)
plt.legend(scennames_labels)
plt.title('Cumulative Virgin Material Extraction: Module')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
plt.ylim(0,)

(0.0, 11.815604480998033)


annual_virgin_module = cc_yearly.filter(like='VirginStock_Module')
plt.plot(annual_virgin_module/1e6)
plt.legend(scennames_labels)
plt.title('Annual Virgin Material Extraction: Module')
plt.ylabel('Virgin Material Requirements\n[million tonnes]')
plt.ylim(0,)

(0.0, 357.5570213228335)


annual_virgin_module_mt = annual_virgin_module/1e6

fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5), sharey=True, sharex=True, 
                                      gridspec_kw={'wspace':0})
#ax1.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax1.set_prop_cycle(color=colorpalette[1:4])
ax1.plot(annual_virgin_module_mt.iloc[:,1:4], label=scennames_labels_flat[1:4])# , color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)
ax1.set_ylabel('Annual Material Demands\n[million metric tonnes]', fontsize=20)
ax1.set_xlim(2000,2100)
ax1.set_ylim(0,350)
ax1.legend(bbox_to_anchor=(0.9,-0.05))

#ax2.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax2.set_prop_cycle(color=colorpalette[4:7])
ax2.plot(annual_virgin_module_mt.iloc[:,4:7], label=scennames_labels_flat[4:7], ls='--') #, color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)
ax2.legend(bbox_to_anchor=(1,-0.05))

#ax3.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax3.set_prop_cycle(color=colorpalette[7:11])
ax3.plot(annual_virgin_module_mt.iloc[:,7:11], label=scennames_labels_flat[7:11])# , color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)
ax3.legend(bbox_to_anchor=(0.9,-0.05))

fig.suptitle('Annual Material Demands', fontsize=24)
plt.show()
fig.savefig('energyresults-annualMatDemands.png', dpi=300, bbox_inches='tight')


#do it by decade to match up with annual installs graphs
virgin_module_annual = annual_virgin_module.loc[2051:,]
virgin_module_decades = virgin_module_annual.groupby(np.arange(len(posttransinstalls_annual))//10).sum()
virgin_module_decades.index = ['2051-2060','2061-2070','2071-2080','2081-2090','2091-2100']
virgin_module_decades
#this is the sum each decade, still need to divide by 10 for annual


annual_virgin_module_decademod = annual_virgin_module.copy()
for col in annual_virgin_module_decademod:
    annual_virgin_module_decademod.loc[2051:2060,col] = virgin_module_decades.loc['2051-2060',col]/10
    annual_virgin_module_decademod.loc[2061:2070,col] = virgin_module_decades.loc['2061-2070',col]/10
    annual_virgin_module_decademod.loc[2071:2080,col] = virgin_module_decades.loc['2071-2080',col]/10
    annual_virgin_module_decademod.loc[2081:2090,col] = virgin_module_decades.loc['2081-2090',col]/10
    annual_virgin_module_decademod.loc[2091:2100,col] = virgin_module_decades.loc['2091-2100',col]/10
    
#plt.plot(annual_virgin_module_decademod)


annual_virgin_module_decademod_mt = annual_virgin_module_decademod/1e6

fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5), sharey=True, sharex=True, 
                                      gridspec_kw={'wspace':0})
#ax1.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax1.set_prop_cycle(color=colorpalette[1:4])
ax1.plot(annual_virgin_module_decademod_mt.iloc[:,1:4], label=scennames_labels_flat[1:4])# , color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)
ax1.set_ylabel('Annual Material Demands\n[million metric tonnes]', fontsize=20)
ax1.set_xlim(2000,2100)
ax1.set_ylim(0,350)
ax1.legend(bbox_to_anchor=(0.9,-0.05))

#ax2.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax2.set_prop_cycle(color=colorpalette[4:7])
ax2.plot(annual_virgin_module_decademod_mt.iloc[:,4:7], label=scennames_labels_flat[4:7], ls='--') #, color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)
ax2.legend(bbox_to_anchor=(1,-0.05))

#ax3.plot(global_projection['World_annual_[MWdc]']/1e6, label='Minimum Deployment', ls='dashdot')
ax3.set_prop_cycle(color=colorpalette[7:11])
ax3.plot(annual_virgin_module_decademod_mt.iloc[:,7:11], label=scennames_labels_flat[7:11])# , color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)
ax3.legend(bbox_to_anchor=(0.9,-0.05))

fig.suptitle('Annual Material Demands Decade Average Post 2050', fontsize=24)
plt.show()
fig.savefig('energyresults-annualMatDemands-decade.png', dpi=300, bbox_inches='tight')


#print out masses for stacked bar charts
#mass_cumu = cc_cumu.loc[[2100]].filter(like='PV_ICE_[Tonnes]')
mass_cumu_virgin = cc_cumu.filter(like='VirginStock').loc[2100]
mass_cumu_wasteall = cc_cumu.filter(like='WasteAll').loc[2100]
mass_cumu = pd.concat([mass_cumu_virgin,mass_cumu_wasteall])
mass_cumu.to_csv(os.path.join(testfolder, 'cc_cumu_mass_2100results.csv'))


cc_cumu.to_csv(os.path.join(testfolder, 'cc_cumu_mass_results.csv'))


cumu_lifecycle_wastes = cc_cumu.filter(like='WasteAll_Module')

plt.bar(scennames_labels, cumu_lifecycle_wastes.loc[2100]/1e9, 
        tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.title('Cumulative Lifecycle Wastes')
plt.ylabel('Lifecycle Wastes\n[billion tonnes]')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, 'Extreme\nLong-Lived'),
  Text(2.0, 0, 'Extreme\nHigh Eff'),
  Text(3.0, 0, 'Extreme\nCircular'),
  Text(4.0, 0, 'Ambitious\n50-year\nPERC'),
  Text(5.0, 0, 'Ambitious\nRecycled Si'),
  Text(6.0, 0, 'Ambitious\nCircular\nPerovskite'),
  Text(7.0, 0, 'PERC'),
  Text(8.0, 0, 'SHJ'),
  Text(9.0, 0, 'TOPCon'),
  Text(10.0, 0, 'IRENA\nreg. loss')])


cumu_lifecycle_wastes_bt = cc_cumu.filter(like='WasteAll_Module')/1e9

fig, (ax0,ax1,ax2,ax3) = plt.subplots(1,4,figsize=(15,6), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [0.5,2,2,2.5]})

ax0.bar(scennames_labels[0:1], cumu_lifecycle_wastes_bt.iloc[-1,0:1], width=0.1, 
        tick_label=scennames_labels[0:1], color=colorpalette[0:1])
ax0.set_ylim(0,7)
ax0.set_ylabel('Lifecycle Wastes\n[billion tonnes]', fontsize=20)
#ax0.set_title('Baseline', fontsize=14)

ax1.bar(scennames_labels[1:4], cumu_lifecycle_wastes_bt.iloc[-1,1:4], width=0.8, 
        tick_label=scennames_labels[1:4], color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)

ax2.bar(scennames_labels[4:7], cumu_lifecycle_wastes_bt.iloc[-1,4:7], width=0.8,
        tick_label=scennames_labels[4:7], color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)

ax3.bar(scennames_labels[7:11], cumu_lifecycle_wastes_bt.iloc[-1,7:11], width=0.8,
        tick_label=scennames_labels[7:11], color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)

fig.suptitle('Cumulative Lifecycle Wastes', fontsize=24)
plt.show()

fig.savefig('energyresults-cumuWaste.png', dpi=300, bbox_inches='tight')


wastes = cumu_lifecycle_wastes_bt.iloc[-1,:]
wastes.index=scennames_labels


annual_waste_module = cc_yearly.filter(like='WasteAll_Module')
plt.plot(annual_waste_module/1e6)
plt.legend(scennames_labels_flat)
plt.title('Annual Waste: Module')
plt.ylabel('Lifecycle Wastes\n[million tonnes]')
plt.ylim(0,)

(0.0, 297.3535841775627)


sim1.scenario['r_IRENA'].dataOut_m.loc[45:,]


sim1.scenario['r_SHJ'].dataOut_e

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [187], in <cell line: 1>()
----> 1 sim1.scenario['r_SHJ'].dataOut_e

AttributeError: 'Scenario' object has no attribute 'dataOut_e'


#energyGen = allenergy.filter(like='e_out_annual')
plt.plot(energyGen/1e12)
plt.legend(scennames_labels)
plt.title('Annual Energy Generation')
plt.ylabel('Energy Generation [TWh]')
plt.ylim(0,)


e_annual_sumDemands = energy_demands.filter(like='demand_total')


plt.plot(e_annual_sumDemands/1e12)
plt.legend(scennames_labels)
plt.title('Annual Lifecycle Energy Demands')
plt.ylabel('Energy Demands\n[TWh]')
plt.ylim(0,)


e_annual_sumDemands_cumu = e_annual_sumDemands.cumsum()


plt.bar(e_annual_sumDemands_cumu.columns, e_annual_sumDemands_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Cumulative Lifecycle Energy Demands')
plt.ylabel('Cumulative Energy Demands\n[TWh]')
plt.xticks(rotation=90)


cumu_e_demands_twh


cumu_e_demands_twh = e_annual_sumDemands_cumu.loc[2100]/1e12

fig, (ax0,ax1,ax2,ax3) = plt.subplots(1,4,figsize=(15,6), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [0.5,2,2,2.5]})

ax0.bar(scennames_labels[0:1], cumu_e_demands_twh.iloc[0:1], width=0.1, 
        tick_label=scennames_labels[0:1], color=colorpalette[0:1])
ax0.set_ylim(0,300000)
ax0.set_ylabel('Cumulative Energy Demands\n[TWh]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)

ax1.bar(scennames_labels[1:4], cumu_e_demands_twh.iloc[1:4], width=0.8, 
        tick_label=scennames_labels[1:4], color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)

ax2.bar(scennames_labels[4:7], cumu_e_demands_twh.iloc[4:7], width=0.8,
        tick_label=scennames_labels[4:7], color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)

ax3.bar(scennames_labels[7:11], cumu_e_demands_twh.iloc[7:11], width=0.8,
        tick_label=scennames_labels[7:11], color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)

fig.suptitle('Cumulative Lifecycle Energy Demands', fontsize=24)
plt.show()

fig.savefig('energyresults-energyDemands.png', dpi=300, bbox_inches='tight')


e_fuels = energy_demands.filter(like='_fuel')
e_energydemands = energy_demands.loc[:,~energy_demands.columns.isin(e_fuels.columns)]
e_mfging_bymat = pd.DataFrame()
for scen in sim1.scenario.keys():
    e_energy_mfg_scen = e_energydemands.filter(like=scen).filter(like='_MFG_')
    e_energy_mod_mfg_scen = energy_demands.filter(like=scen).filter(like='mod_MFG')
    e_mfging_bymat = pd.concat([e_mfging_bymat,e_energy_mfg_scen,e_energy_mod_mfg_scen], axis=1)


cum_e_mfging_bymat = e_mfging_bymat.cumsum().loc[2100]
cum_e_mfging_bymat


#run 2x rn??
#energy demands by material mfging
plt.bar(e_annual_sumDemands_cumu.columns, e_annual_sumDemands_cumu.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)

#Materials
cume_mfg_glass = cum_e_mfging_bymat.filter(like='glass')
cume_mfg_silicon = cum_e_mfging_bymat.filter(like='silicon')
cume_mfg_silver = cum_e_mfging_bymat.filter(like='silver')
cume_mfg_aluminium_frames = cum_e_mfging_bymat.filter(like='aluminium_frames')
cume_mfg_copper = cum_e_mfging_bymat.filter(like='copper')
cume_mfg_encapsulant = cum_e_mfging_bymat.filter(like='encapsulant')
cume_mfg_backsheet = cum_e_mfging_bymat.filter(like='backsheet')
cume_mfg_mod = cum_e_mfging_bymat.filter(like='mod')

bot_ag = cume_mfg_glass.values+cume_mfg_silicon.values
bot_al = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values
bot_cu = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values
bot_encap = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values+cume_mfg_copper.values
bot_back = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values+cume_mfg_copper.values+cume_mfg_encapsulant.values
bot_mod = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values+cume_mfg_copper.values+cume_mfg_encapsulant.values+cume_mfg_backsheet.values

plt.bar(scennames_labels, cume_mfg_glass, color = colormats[0])
plt.bar(scennames_labels, cume_mfg_silicon, bottom = cume_mfg_glass, color=colormats[1])
plt.bar(scennames_labels, cume_mfg_silver, bottom = bot_ag, color=colormats[2])
plt.bar(scennames_labels, cume_mfg_aluminium_frames, bottom = bot_al, color=colormats[3])
plt.bar(scennames_labels, cume_mfg_copper, bottom = bot_cu , color=colormats[4])
plt.bar(scennames_labels, cume_mfg_encapsulant, bottom = bot_encap ,color=colormats[5])
plt.bar(scennames_labels, cume_mfg_backsheet, bottom = bot_back, color=colormats[6])
plt.bar(scennames_labels, cume_mfg_mod, bottom = bot_back, color='white')

plt.xticks(rotation=90)

plt.legend(MATERIALS)


energyGen_cumu = energyGen.cumsum()
energyGen_cumu.columns = e_annual_sumDemands_cumu.columns = scennames_labels
netEnergy_cumu = energyGen_cumu.loc[[2100]] - e_annual_sumDemands_cumu.loc[[2100]]


e_annual_sumDemands_cumu.loc[[2100]]


netEnergy_cumu


plt.bar(netEnergy_cumu.columns, netEnergy_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy [TWh]')
#plt.ylim(4e6,5.5e6)
plt.xticks(rotation=90)


cumu_netEnergy_twh = netEnergy_cumu.loc[2100]/1e15

fig, (ax0,ax1,ax2,ax3) = plt.subplots(1,4,figsize=(15,6), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [0.5,2,2,2.5]})

ax0.bar(scennames_labels[0:1], cumu_netEnergy_twh.iloc[0:1], width=0.1, 
        tick_label=scennames_labels[0:1], color=colorpalette[0:1])
ax0.set_ylim(0,8000)
ax0.set_ylabel('Cumulative Net Energy [PetaWh]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)

ax1.bar(scennames_labels[1:4], cumu_netEnergy_twh.iloc[1:4], width=0.8, 
        tick_label=scennames_labels[1:4], color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)

ax2.bar(scennames_labels[4:7], cumu_netEnergy_twh.iloc[4:7], width=0.8,
        tick_label=scennames_labels[4:7], color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)

ax3.bar(scennames_labels[7:11], cumu_netEnergy_twh.iloc[7:11], width=0.8,
        tick_label=scennames_labels[7:11], color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)

fig.suptitle('Net Energy Cumulatively', fontsize=24)
plt.show()

fig.savefig('energyresults-cumuNetEnergy.png', dpi=300, bbox_inches='tight')


netEnergy_relative = netEnergy_cumu - netEnergy_cumu.loc[2100,'PV_ICE']
netEnergy_relative
plt.bar(netEnergy_relative.columns, netEnergy_relative.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Cumulatively Net Energy Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy [TWh]')
plt.xticks(rotation=90)


netEnergy_cumu_norm = netEnergy_cumu/netEnergy_cumu.loc[2100,'PV_ICE']
netEnergy_cumu_norm_waterfall = netEnergy_cumu_norm-1
netEnergy_cumu_norm


plt.bar(netEnergy_cumu.columns, netEnergy_cumu_norm_waterfall.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy')
#plt.ylim(-0.026,0.005)
plt.plot(0.0, lw=2)
plt.xticks(rotation=90)


fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(14,6), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [2,2,2.5]})

#ax0.bar(scennames_labels[0:1], netEnergy_cumu_norm_waterfall.iloc[-1,0:1], width=0.1, 
#        tick_label=scennames_labels[0:1], color=colorpalette[0:1])

#ax0.set_title('Baseline', fontsize=14)

ax1.bar(scennames_labels[1:4], netEnergy_cumu_norm_waterfall.iloc[-1,1:4], width=0.8, 
        tick_label=scennames_labels[1:4], color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)
ax1.set_ylim(-.03,.1)
ax1.set_ylabel('Unitless', fontsize=20)
ax1.axhline(0, lw=1, color='black')

ax2.bar(scennames_labels[4:7], netEnergy_cumu_norm_waterfall.iloc[-1,4:7], width=0.8,
        tick_label=scennames_labels[4:7], color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)
ax2.axhline(0, lw=1, color='black')

ax3.bar(scennames_labels[7:11], netEnergy_cumu_norm_waterfall.iloc[-1,7:11], width=0.8,
        tick_label=scennames_labels[7:11], color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)
ax3.axhline(0, lw=1, color='black')

fig.suptitle('Normalized Net Energy', fontsize=24)
plt.show()

fig.savefig('energyresults-cumuNetEnergyNorm.png', dpi=300, bbox_inches='tight')


netenergy_final = netEnergy_cumu.loc[2100]/1e12


plt.bar(netEnergy_cumu.columns, netEnergy_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy\n[TWh]')
plt.xticks(rotation=90)


energyGen.columns = e_annual_sumDemands.columns = scennames_labels
annual_net_energy = energyGen - e_annual_sumDemands


plt.plot(annual_net_energy/1e12)
plt.legend(scennames_labels_flat)
plt.title('Annual Net Energy')
plt.ylabel('Net Energy (Eout - Ein)\n[TWh]')
plt.ylim(0,)
plt.xlim(2000,2100)


energyBalance_allyears = energyGen_cumu/e_annual_sumDemands_cumu


plt.bar(energyBalance_allyears.columns, energyBalance_allyears.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('EROI of all Deployments')
plt.ylabel('Unitless')
plt.xticks(rotation=90)


energyBalance = energyBalance_allyears.loc[2100]

fig, (ax0,ax1,ax2,ax3) = plt.subplots(1,4,figsize=(15,6), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [0.5,2,2,2.5]})

ax0.bar(scennames_labels[0:1], energyBalance.iloc[0:1], width=0.1, 
        tick_label=scennames_labels[0:1], color=colorpalette[0:1])
ax0.set_ylim(0,70)
ax0.set_ylabel('Unitless', fontsize=20)
ax0.set_title('Baseline', fontsize=14)

ax1.bar(scennames_labels[1:4], energyBalance.iloc[1:4], width=0.8, 
        tick_label=scennames_labels[1:4], color=colorpalette[1:4])
ax1.set_title('Idealized', fontsize=14)

ax2.bar(scennames_labels[4:7], energyBalance.iloc[4:7], width=0.8,
        tick_label=scennames_labels[4:7], color=colorpalette[4:7])
ax2.set_title('Ambitious', fontsize=14)

ax3.bar(scennames_labels[7:11], energyBalance.iloc[7:11], width=0.8,
        tick_label=scennames_labels[7:11], color=colorpalette[7:11])
ax3.set_title('Commercialized', fontsize=14)

fig.suptitle('Energy Balance', fontsize=24)
plt.show()

fig.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')


discussTable = pd.concat([replacements,virgin,wastes, cumu_e_demands_twh,netenergy_final,energyBalance], axis=1,
         keys=['replacements','virgin','wastes','energydemand','netenergy','energybalance']).T
discussTable.to_csv(os.path.join(testfolder,'discussiontable.csv'))
discussTable


cumu_lifecycle_wastes_bt

	E_MFG_kWhpkg	E_mfgFuelFraction
1995	600.817735	0.064489
1996	527.042434	0.072326
1997	485.167804	0.077357
1998	432.693843	0.085473
1999	380.620553	0.095834
2000	269.697933	0.075408
2001	236.759317	0.066235
2002	204.221370	0.054188
2003	172.084094	0.037719
2004	159.669155	0.030097
2005	140.254886	0.022532
2006	130.586287	0.022463
2007	121.318358	0.022639
2008	112.451100	0.023120
2009	103.984511	0.023976
2010	95.918593	0.025298
2011	88.253345	0.027195
2012	88.388767	0.027307
2013	88.924859	0.027745
2014	89.861622	0.028498
2015	91.199054	0.029547
2016	76.337157	0.037576
2017	61.875930	0.049814
2018	62.915373	0.053028
2019	64.355487	0.056410
2020	74.096270	0.053503
2021	77.837724	0.055738
2022	77.837724	0.055738
2023	77.837724	0.055738
2024	77.837724	0.055738
2025	77.837724	0.055738
2026	77.837724	0.055738
2027	77.837724	0.055738
2028	77.837724	0.055738
2029	77.837724	0.055738
2030	77.837724	0.055738
2031	77.837724	0.055738
2032	77.837724	0.055738
2033	77.837724	0.055738
2034	77.837724	0.055738
2035	77.837724	0.055738
2036	77.837724	0.055738
2037	77.837724	0.055738
2038	77.837724	0.055738
2039	77.837724	0.055738
2040	77.837724	0.055738
2041	77.837724	0.055738
2042	77.837724	0.055738
2043	77.837724	0.055738
2044	77.837724	0.055738
2045	77.837724	0.055738
2046	77.837724	0.055738
2047	77.837724	0.055738
2048	77.837724	0.055738
2049	77.837724	0.055738
2050	77.837724	0.055738

	year	e_mat_extraction	e_mat_MFG	e_mat_MFG_fuelfraction	e_mat_MFGScrap_LQ	e_mat_MFGScrap_HQ	e_mat_Landfill	e_mat_EoL_ReMFG_clean	e_mat_Recycled_LQ	e_mat_Recycled_HQ	e_mat_Recycled_HQ_fuelfraction
25	2020	0.0062	169.06	0.22	24.5	600.0	0.09	0.4	1.6	138.380000	0.200000
26	2021	0.0062	170.73	0.21	24.5	600.0	0.09	0.4	1.6	143.040000	0.190000
27	2022	0.0062	169.05	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738
28	2023	0.0062	168.80	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738
29	2024	0.0062	169.09	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738

	newInstalledCapacity_sim1_PV_ICE_[MW]	newInstalledCapacity_sim1_ex_PERC_50_[MW]	newInstalledCapacity_sim1_ex_High_eff_[MW]	newInstalledCapacity_sim1_ex_Perovskite_[MW]	newInstalledCapacity_sim1_h_50PERC_[MW]	newInstalledCapacity_sim1_h_RecycledPERC_[MW]	newInstalledCapacity_sim1_h_Perovskite_[MW]	newInstalledCapacity_sim1_r_PERC_[MW]	newInstalledCapacity_sim1_r_SHJ_[MW]	newInstalledCapacity_sim1_r_TOPCon_[MW]	newInstalledCapacity_sim1_r_IRENA_[MW]
2051-2060	8.084245e+06	5.666005e+06	2.208229e+07	4.982676e+07	5.844223e+06	1.263023e+07	3.114650e+07	1.169165e+07	1.169165e+07	1.169165e+07	2.483895e+07
2061-2070	2.017992e+07	5.692660e+06	3.796350e+07	6.011196e+07	7.795488e+06	2.852800e+07	4.786938e+07	1.655666e+07	1.655666e+07	1.655666e+07	4.030092e+07
2071-2080	3.606240e+07	1.102867e+07	3.783553e+07	6.575617e+07	1.288077e+07	3.175182e+07	2.558590e+07	3.546531e+07	3.546531e+07	3.546531e+07	3.909426e+07
2081-2090	2.952406e+07	2.509858e+07	3.325669e+07	5.794755e+07	2.368093e+07	2.700252e+07	4.462791e+07	2.832288e+07	2.832288e+07	2.832288e+07	3.731363e+07
2091-2100	1.720211e+07	3.965977e+07	4.749961e+07	6.454053e+07	3.763053e+07	2.191274e+07	3.635409e+07	1.596166e+07	1.596166e+07	1.596166e+07	4.634064e+07

	VirginStock_Module_sim1_PV_ICE_[Tonnes]	VirginStock_Module_sim1_ex_PERC_50_[Tonnes]	VirginStock_Module_sim1_ex_High_eff_[Tonnes]	VirginStock_Module_sim1_ex_Perovskite_[Tonnes]	VirginStock_Module_sim1_h_50PERC_[Tonnes]	VirginStock_Module_sim1_h_RecycledPERC_[Tonnes]	VirginStock_Module_sim1_h_Perovskite_[Tonnes]	VirginStock_Module_sim1_r_PERC_[Tonnes]	VirginStock_Module_sim1_r_SHJ_[Tonnes]	VirginStock_Module_sim1_r_TOPCon_[Tonnes]	VirginStock_Module_sim1_r_IRENA_[Tonnes]
2051-2060	4.220275e+08	3.352095e+08	8.804683e+08	4.952477e+08	2.895188e+08	5.847210e+08	3.552753e+08	4.970195e+08	4.723163e+08	4.823976e+08	8.900923e+08
2061-2070	1.052121e+09	3.368010e+08	1.513117e+09	4.466704e+08	3.754950e+08	1.261661e+09	3.242539e+08	6.230063e+08	5.922935e+08	6.049473e+08	1.286893e+09
2071-2080	1.879663e+09	6.516744e+08	1.508035e+09	4.296153e+08	5.984333e+08	1.409047e+09	3.675362e+08	1.110113e+09	1.055385e+09	1.077932e+09	1.284840e+09
2081-2090	1.539121e+09	1.481997e+09	1.325661e+09	5.013477e+08	1.070696e+09	1.202020e+09	2.986008e+08	9.343835e+08	8.883211e+08	9.072984e+08	1.254326e+09
2091-2100	8.971855e+08	2.341286e+09	1.893029e+09	4.772295e+08	1.679661e+09	9.895179e+08	3.420519e+08	6.160021e+08	5.856363e+08	5.981472e+08	1.482761e+09

	Area	Cumulative_Active_Area	EOL_BadStatus	EOL_Landfill0	EOL_PATHS	EOL_PG	Effective_Capacity_[W]	Landfill_0_ProjLife	...	Yearly_Sum_Area_PathsBad	Yearly_Sum_Area_PathsGood	Yearly_Sum_Area_atEOL	Yearly_Sum_Power_EOLby_Degradation	Yearly_Sum_Power_EOLby_Failure	Yearly_Sum_Power_EOLby_ProjectLifetime	Yearly_Sum_Power_PathsBad	Yearly_Sum_Power_PathsGood	Yearly_Sum_Power_atEOL	irradiance_stc
45	1.887576e+10	2.426090e+11	6.107428e+08	3.472851e+08	6.107428e+08	0.000000e+00	4.759655e+13	0.000000e+00	...	9.580280e+08	0.000000e+00	9.580280e+08	0.000000e+00	1.620390e+11	0.000000e+00	1.620390e+11	0.000000e+00	1.620390e+11	1000.0
46	1.959160e+10	2.607759e+11	7.627189e+08	4.844115e+08	9.403282e+08	1.776093e+08	5.118580e+13	9.149572e+07	...	1.155635e+09	1.776093e+08	1.424740e+09	0.000000e+00	1.959023e+11	3.828953e+10	1.959023e+11	2.527109e+10	2.341918e+11	1000.0
47	2.030250e+10	2.790033e+11	1.416258e+09	6.588455e+08	1.416258e+09	0.000000e+00	5.479611e+13	0.000000e+00	...	2.075104e+09	0.000000e+00	2.075104e+09	1.169787e+11	2.332548e+11	0.000000e+00	3.502335e+11	0.000000e+00	2.332548e+11	1000.0
48	2.100853e+10	2.968673e+11	2.216851e+09	9.276185e+08	2.216851e+09	0.000000e+00	5.838736e+13	0.000000e+00	...	3.144469e+09	0.000000e+00	3.144469e+09	2.405238e+11	2.657223e+11	0.000000e+00	5.062461e+11	0.000000e+00	2.657223e+11	1000.0
49	2.170972e+10	3.146111e+11	2.885204e+09	1.080712e+09	2.885204e+09	0.000000e+00	6.198102e+13	0.000000e+00	...	3.965916e+09	0.000000e+00	3.965916e+09	3.436218e+11	2.973211e+11	0.000000e+00	6.409429e+11	0.000000e+00	2.973211e+11	1000.0
50	2.240613e+10	3.322203e+11	3.597770e+09	1.199257e+09	3.597770e+09	0.000000e+00	6.557324e+13	0.000000e+00	...	4.797027e+09	0.000000e+00	4.797027e+09	4.521279e+11	3.273195e+11	0.000000e+00	7.794473e+11	0.000000e+00	3.273195e+11	1000.0
51	9.969091e+08	3.276373e+11	4.184910e+09	1.394970e+09	4.184910e+09	0.000000e+00	6.428971e+13	0.000000e+00	...	5.579880e+09	0.000000e+00	5.579880e+09	5.561901e+11	3.565742e+11	0.000000e+00	9.127643e+11	0.000000e+00	3.565742e+11	1000.0
52	9.969091e+08	3.225390e+11	4.571381e+09	1.523794e+09	4.571381e+09	0.000000e+00	6.292871e+13	0.000000e+00	...	6.095175e+09	0.000000e+00	6.095175e+09	6.084761e+11	3.932396e+11	0.000000e+00	1.001716e+12	0.000000e+00	3.932396e+11	1000.0
53	9.969091e+08	3.166512e+11	5.163523e+09	1.721174e+09	5.163523e+09	0.000000e+00	6.144326e+13	0.000000e+00	...	6.884697e+09	0.000000e+00	6.884697e+09	7.085070e+11	4.299028e+11	0.000000e+00	1.138410e+12	0.000000e+00	4.299028e+11	1000.0
54	9.969091e+08	3.099852e+11	5.747217e+09	1.915739e+09	5.747217e+09	0.000000e+00	5.983450e+13	0.000000e+00	...	7.662956e+09	0.000000e+00	7.662956e+09	8.085378e+11	4.665505e+11	0.000000e+00	1.275088e+12	0.000000e+00	4.665505e+11	1000.0
55	9.969091e+08	3.025514e+11	6.323014e+09	2.107671e+09	6.323014e+09	0.000000e+00	5.810360e+13	0.000000e+00	...	8.430685e+09	0.000000e+00	8.430685e+09	9.085686e+11	5.031420e+11	0.000000e+00	1.411711e+12	0.000000e+00	5.031420e+11	1000.0
56	9.969091e+08	2.943289e+11	6.914582e+09	2.304861e+09	6.914582e+09	0.000000e+00	5.625180e+13	0.000000e+00	...	9.219443e+09	0.000000e+00	9.219443e+09	1.008599e+12	5.395906e+11	0.000000e+00	1.548190e+12	0.000000e+00	5.395906e+11	1000.0
57	9.969091e+08	2.853247e+11	7.500791e+09	2.500264e+09	7.500791e+09	0.000000e+00	5.428048e+13	0.000000e+00	...	1.000106e+10	0.000000e+00	1.000106e+10	1.108630e+12	5.757408e+11	0.000000e+00	1.684371e+12	0.000000e+00	5.757408e+11	1000.0
58	9.969091e+08	2.755473e+11	8.080778e+09	2.693593e+09	8.080778e+09	0.000000e+00	5.219127e+13	0.000000e+00	...	1.077437e+10	0.000000e+00	1.077437e+10	1.208661e+12	6.113456e+11	0.000000e+00	1.820007e+12	0.000000e+00	6.113456e+11	1000.0
59	9.969091e+08	2.650065e+11	8.653231e+09	2.884410e+09	8.653231e+09	0.000000e+00	4.998613e+13	0.000000e+00	...	1.153764e+10	0.000000e+00	1.153764e+10	1.308692e+12	6.460406e+11	0.000000e+00	1.954733e+12	0.000000e+00	6.460406e+11	1000.0
60	9.969091e+08	2.537151e+11	9.216280e+09	3.072093e+09	9.216280e+09	0.000000e+00	4.766754e+13	0.000000e+00	...	1.228837e+10	0.000000e+00	1.228837e+10	1.408723e+12	6.793186e+11	0.000000e+00	2.088041e+12	0.000000e+00	6.793186e+11	1000.0
61	9.969091e+08	2.416888e+11	9.767380e+09	3.255793e+09	9.767380e+09	0.000000e+00	4.523859e+13	0.000000e+00	...	1.302317e+10	0.000000e+00	1.302317e+10	1.508754e+12	7.105027e+11	0.000000e+00	2.219256e+12	0.000000e+00	7.105027e+11	1000.0
62	9.969091e+08	2.289481e+11	1.030320e+10	3.434400e+09	1.030320e+10	0.000000e+00	4.270326e+13	0.000000e+00	...	1.373760e+10	0.000000e+00	1.373760e+10	1.608784e+12	7.387215e+11	0.000000e+00	2.347506e+12	0.000000e+00	7.387215e+11	1000.0
63	9.969091e+08	2.155190e+11	1.081950e+10	3.606500e+09	1.081950e+10	0.000000e+00	4.006655e+13	0.000000e+00	...	1.442600e+10	0.000000e+00	1.442600e+10	1.708815e+12	7.628847e+11	0.000000e+00	2.471700e+12	0.000000e+00	7.628847e+11	1000.0
64	9.969091e+08	2.014345e+11	1.131104e+10	3.770348e+09	1.131104e+10	0.000000e+00	3.733476e+13	0.000000e+00	...	1.508139e+10	0.000000e+00	1.508139e+10	1.808846e+12	7.816634e+11	0.000000e+00	2.590509e+12	0.000000e+00	7.816634e+11	1000.0
65	9.969091e+08	1.867361e+11	1.177149e+10	3.923832e+09	1.177149e+10	0.000000e+00	3.451572e+13	0.000000e+00	...	1.569533e+10	0.000000e+00	1.569533e+10	1.908877e+12	7.934764e+11	0.000000e+00	2.702353e+12	0.000000e+00	7.934764e+11	1000.0
66	9.969091e+08	1.714752e+11	1.219338e+10	4.064462e+09	1.219338e+10	0.000000e+00	3.161900e+13	0.000000e+00	...	1.625785e+10	0.000000e+00	1.625785e+10	2.008908e+12	7.964852e+11	0.000000e+00	2.805393e+12	0.000000e+00	7.964852e+11	1000.0
67	9.969091e+08	1.557146e+11	1.256811e+10	4.189369e+09	1.256811e+10	0.000000e+00	2.865621e+13	0.000000e+00	...	1.675747e+10	0.000000e+00	1.675747e+10	2.108938e+12	7.886031e+11	0.000000e+00	2.897541e+12	0.000000e+00	7.886031e+11	1000.0
68	9.969091e+08	1.395302e+11	1.288599e+10	4.295329e+09	1.288599e+10	0.000000e+00	2.564113e+13	0.000000e+00	...	1.718132e+10	0.000000e+00	1.718132e+10	2.208969e+12	7.675215e+11	0.000000e+00	2.976491e+12	0.000000e+00	7.675215e+11	1000.0
69	9.969091e+08	1.230118e+11	1.313647e+10	4.378823e+09	1.313647e+10	0.000000e+00	2.258992e+13	0.000000e+00	...	1.751529e+10	0.000000e+00	1.751529e+10	2.309000e+12	7.307601e+11	0.000000e+00	3.039760e+12	0.000000e+00	7.307601e+11	1000.0
70	9.969091e+08	1.062642e+11	1.330839e+10	4.436128e+09	1.330839e+10	0.000000e+00	1.952116e+13	0.000000e+00	...	1.774451e+10	0.000000e+00	1.774451e+10	2.409031e+12	6.757457e+11	0.000000e+00	3.084777e+12	0.000000e+00	6.757457e+11	1000.0
71	9.969091e+08	8.940723e+10	1.339043e+10	4.463476e+09	1.339043e+10	0.000000e+00	1.645580e+13	0.000000e+00	...	1.785390e+10	0.000000e+00	1.785390e+10	2.509062e+12	5.999239e+11	0.000000e+00	3.108986e+12	0.000000e+00	5.999239e+11	1000.0
72	9.969091e+08	7.257516e+10	1.337174e+10	4.457246e+09	1.337174e+10	0.000000e+00	1.341702e+13	0.000000e+00	...	1.782898e+10	0.000000e+00	1.782898e+10	2.609093e+12	5.009081e+11	0.000000e+00	3.110001e+12	0.000000e+00	5.009081e+11	1000.0
73	9.969091e+08	5.591512e+10	1.324271e+10	4.414237e+09	1.324271e+10	0.000000e+00	1.042980e+13	0.000000e+00	...	1.765695e+10	0.000000e+00	1.765695e+10	2.709123e+12	3.766648e+11	0.000000e+00	3.085788e+12	0.000000e+00	3.766648e+11	1000.0
74	9.969091e+08	3.958410e+10	1.299594e+10	4.331981e+09	1.299594e+10	0.000000e+00	7.520366e+12	0.000000e+00	...	1.732793e+10	0.000000e+00	1.732793e+10	2.809154e+12	2.257297e+11	0.000000e+00	3.034884e+12	0.000000e+00	2.257297e+11	1000.0
75	9.969091e+08	2.374461e+10	1.262730e+10	4.209101e+09	1.262730e+10	0.000000e+00	4.715373e+12	0.000000e+00	...	1.683640e+10	0.000000e+00	1.683640e+10	2.909185e+12	4.744419e+10	0.000000e+00	2.956629e+12	0.000000e+00	4.744419e+10	1000.0
76	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
77	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
78	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
79	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
80	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
81	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
82	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
83	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
84	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
85	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
86	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
87	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
88	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
89	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
90	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
91	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
92	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
93	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
94	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
95	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
96	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
97	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
98	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
99	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0
100	9.969091e+08	2.374461e+10	7.476818e+08	2.492273e+08	7.476818e+08	0.000000e+00	4.715373e+12	0.000000e+00	...	9.969091e+08	0.000000e+00	9.969091e+08	1.294374e+11	4.744419e+10	0.000000e+00	1.768816e+11	0.000000e+00	4.744419e+10	1000.0

Energy Analysis¶

1. Module & Scenario Creation¶

1.1 Extreme modules¶

1.1.1 Ideal Lifetime - 50 year module¶

1.1.2 Ideal High Efficiency¶

1.1.3 Ideal Circular - Tandem Perovskite¶

2022 module eff = 17.9% #https://www.nrel.gov/pv/assets/pdfs/champion-module-efficiencies-rev220401b.pdf ¶

2050 module eff = 27.3% # Sofia et al 2019 4T future¶

Lightweighting¶

1.2 Hypothetical Modules¶

1.2.1 PERC gradual improvement to 50 year lifetime¶

1.2.2 Recycled Silicon PERC¶

1.2.3 Hopeful Perovskite¶

1.3 Commercialized Modules with Expected Continued Improvement¶

1.3.1 PERC¶

1.3.2 SHJ¶

1.3.3 TOPCon¶

1.3.4 IRENA Reg. Loss with improved recycling¶

1.4 Modify Scenarios¶

1.4.1 Time Period for study: 2000 to 2100¶

1.4.2 Apply deployment curve¶

2. Calculate Mass flow: Identical Installations¶

evolving weibulls¶

evolve_weibull_temp = evolve_weibull.reset_index(drop=True)¶

evolve_weibull_temp.to_dict(orient='dict')¶

dict([(i,[a,b,c ]) for i, a,b,c in zip(df.ID, df.A,df.B,df.C)])¶

3. Calculate Mass and Energy Flow: Installation Compensation¶

RESULTS: Effective Capacity and Replacements¶

Fixing Jagged Lines¶

RESULTS: Virgin Material Demands¶

RESULTS: Lifecycle Wastes¶

RESULTS: Energy¶

Energy Generation¶

Net Energy¶

Energy Balance¶

Discussion summary table¶

Energy Analysis¶

1. Module & Scenario Creation¶

1.1 Extreme modules¶

1.1.1 Ideal Lifetime - 50 year module¶

1.1.2 Ideal High Efficiency¶

1.1.3 Ideal Circular - Tandem Perovskite¶

2022 module eff = 17.9% #https://www.nrel.gov/pv/assets/pdfs/champion-module-efficiencies-rev220401b.pdf¶

2050 module eff = 27.3% # Sofia et al 2019 4T future¶

Lightweighting¶

1.2 Hypothetical Modules¶

1.2.1 PERC gradual improvement to 50 year lifetime¶

1.2.2 Recycled Silicon PERC¶

1.2.3 Hopeful Perovskite¶

1.3 Commercialized Modules with Expected Continued Improvement¶

1.3.1 PERC¶

1.3.2 SHJ¶

1.3.3 TOPCon¶

1.3.4 IRENA Reg. Loss with improved recycling¶

1.4 Modify Scenarios¶

1.4.1 Time Period for study: 2000 to 2100¶

1.4.2 Apply deployment curve¶

2. Calculate Mass flow: Identical Installations¶

evolving weibulls¶

evolve_weibull_temp = evolve_weibull.reset_index(drop=True)¶

evolve_weibull_temp.to_dict(orient='dict')¶

dict([(i,[a,b,c ]) for i, a,b,c in zip(df.ID, df.A,df.B,df.C)])¶

3. Calculate Mass and Energy Flow: Installation Compensation¶

RESULTS: Effective Capacity and Replacements¶

Fixing Jagged Lines¶

RESULTS: Virgin Material Demands¶

RESULTS: Lifecycle Wastes¶

RESULTS: Energy¶

Energy Generation¶

Net Energy¶

Energy Balance¶

Discussion summary table¶

2022 module eff = 17.9% #https://www.nrel.gov/pv/assets/pdfs/champion-module-efficiencies-rev220401b.pdf ¶